James D. Weiland

University of Southern California, University of Michigan–Ann Arbor

Papers

Total Citations

135

H-Index

About

James D. Weiland is a pioneering researcher at the intersection of assistive technology, computer vision, and embedded systems. His primary research areas include robotic vision for the visually impaired, semantic scene understanding, and the optimization of deep neural networks for low-compute devices. Weiland’s most influential contribution is the development of a head-mounted, stereo-vision navigational aid for the visually impaired, which allows users to stand and scan their environment for wide-field spatial awareness—a significant ergonomic and functional improvement over traditional waist- or shoulder-mounted systems. This seminal work has garnered 124 citations, underscoring its impact on assistive robotics and human-computer interaction. More recently, Weiland has advanced lightweight semantic segmentation networks, enabling real-time scene understanding on power-constrained platforms like wearable devices, drones, and small mobile robots. His 2023 and 2022 papers on this topic address the critical challenge of deploying deep convolutional neural networks in embedded environments, paving the way for smarter, more autonomous systems. Through his work, Weiland is shaping the future of accessible, intelligent vision systems for both human assistance and robotic navigation.